MatClaw shows a code-first LLM agent autonomously generating and executing workflows for ML force field training, Curie temperature prediction, and parameter search on CuInP2S6, succeeding on code but requiring interventions for tacit domain knowledge.
cAST : Enhancing code retrieval-augmented generation with structural chunking via abstract syntax tree, 2025 b
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
VF-Coder raises GUI code success rate from 21.68% to 28.29% and visual score from 0.4284 to 0.5584 on a new 984-task benchmark by adding direct visual perception and interaction.
BLAgent achieves over 78% Top-1 accuracy on SWE-bench Lite for file-level bug localization using agentic RAG, at 18x lower cost than baselines, and boosts end-to-end APR success by over 20%.
STC reduces tabular chunk counts by up to 56% versus baselines and raises hybrid MRR to 0.5945 and BM25 Recall@1 to 0.754 by preserving row structure during chunking.
citing papers explorer
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MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration
MatClaw shows a code-first LLM agent autonomously generating and executing workflows for ML force field training, Curie temperature prediction, and parameter search on CuInP2S6, succeeding on code but requiring interventions for tacit domain knowledge.
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Coding with Eyes: Visual Feedback Unlocks Reliable GUI Code Generating and Debugging
VF-Coder raises GUI code success rate from 21.68% to 28.29% and visual score from 0.4284 to 0.5584 on a new 984-task benchmark by adding direct visual perception and interaction.
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BLAgent: Agentic RAG for File-Level Bug Localization
BLAgent achieves over 78% Top-1 accuracy on SWE-bench Lite for file-level bug localization using agentic RAG, at 18x lower cost than baselines, and boosts end-to-end APR success by over 20%.
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Structure-Aware Chunking for Tabular Data in Retrieval-Augmented Generation
STC reduces tabular chunk counts by up to 56% versus baselines and raises hybrid MRR to 0.5945 and BM25 Recall@1 to 0.754 by preserving row structure during chunking.